How Big Data Informs Us About Cataract Surgery: The LXXII Edward Jackson Memorial Lecture.

Area of Science:

• Ophthalmology
• Health Informatics
• Big Data Analytics

Background:

• Evaluating quality of care in ophthalmology is crucial for patient outcomes.
• The American Academy of Ophthalmology's Intelligent Research in Sight (IRIS) Registry offers a valuable dataset for quality assessment.
• Big Data analytics presents new avenues for understanding and improving ophthalmic care.

Purpose of the Study:

• To characterize the role of Big Data in evaluating ophthalmology quality of care.
• To identify opportunities for quality improvement using the IRIS Registry data.
• To investigate rare events, such as endophthalmitis following cataract surgery, using Big Data.

Main Methods:

• Review of published literature on Big Data in ophthalmology.
• Analysis of public-use Medicare claims data (2010-2013).
• Analysis of IRIS Registry data (2013-2014) using statistical methods for observational data.

Main Results:

• Endophthalmitis rates post-cataract surgery were 0.14% in Medicare data and 0.08% in IRIS Registry data.
• Higher endophthalmitis rates were observed in eyes with combined cataract surgery and anterior vitrectomy.
• 18.3% of eyes in the IRIS Registry had visual acuity worse than 20/40 one month post-cataract surgery.

Conclusions:

• Big Data from Medicare and IRIS Registry facilitates identification of quality improvement areas, like suboptimal visual acuity post-surgery.
• Tracking patient outcomes in Big Data enables research on rare complications and uncommon procedures.
• Privacy and data security are significant considerations when utilizing Big Data in healthcare.